1. Field of the Invention
The present invention generally relates to methods and systems for automated or semi-automated analysis of digitized radiographs for research and various other investigational uses and, more specifically, to method and systems for diagnosis and tracking of osteoporosis.
2. Description of Related Art
Osteoporosis is a common skeletal disorder characterized by a decrease in bone mass, leading to bone fragility and an increased risk of fractures. There are about 10 million Americans over the age of 50 with osteoporosis. Thirty-four million more have low bone mass of the hip, which puts them at high risk for osteoporotic fractures and related complications later in life. Although effective diagnostic methods and treatments are available, only about 1 in 5 patients indicated for diagnostic evaluation gets tested. Only about 1 in 3 patients who is diagnosed with osteoporosis is offered treatment. Of those on treatment, compliance is low because of side effects of drugs and poor monitoring tools. Of those who start treatment, many stop treatment after 6 months, although it takes 2-3 years for the treatment to be effective.
In the US, osteoporosis resulted in 2 million fractures in 2005, at a direct cost to society of $17 billion. With the aging population, both fractures and costs are projected to rise 50% by 2025. Better tools are needed to diagnose patients, treat those who can benefit from treatment, make sure that the treatment is effective, and to ensure that patients comply with treatment.
Any bone can be affected by osteoporosis, but the fractures typically occur in the hip, spine (vertebrae), and wrist. Although hip fractures are the most serious in terms of morbidity and mortality, vertebral fractures are most important for the diagnosis and prognosis of disease.
Vertebral fractures can be asymptomatic, but can have serious consequences including severe acute and chronic back pain, back deformity, and increased mortality. Furthermore, vertebral fractures are the most common osteoporotic fracture, they occur in younger patients, and their presence is known to be a good indicator for the risk of future spine and hip fractures. This makes the presence of vertebral fractures an important factor in clinical decision making and the primary endpoint in many clinical trials to assess osteoporosis incidence and monitor its progression. In fact, it is in the accurate diagnosis of asynpitomatic vertebral fractures that radiologists make perhaps the most significant contribution to osteoporotic patient care.
In everyday clinical practice, vertebral fractures are usually diagnosed by visual inspection of the patient's spinal radiographs. However, this qualitative approach to identify vertebral fractures is regarded as subjective and therefore may lead to disagreement, especially when performed by inexperienced observers. Precise and accurate visual scoring of vertebral x-rays is a complex, time-consuming process that requires highly specialized training and expertise to perform. For this reason, access to quantitative objective scores has not been generally available to the physician in the point-of-care setting. Instead, most physicians have been forced to rely only on narrative interpretations when making treatment decisions. As a consequence, they sometimes miss mild vertebral fractures, which can lead to inadequate—and even inappropriate—treatment.
For epidemiological studies and clinical drug trials in osteoporosis research, objective and reproducible results are required. Therefore more than a decade ago the semiquantitative (SQ) method was proposed by Genant et al. in J Bone Miner Res, 1993, 8:1137-1148; and the quantitative (e.g., vertebral morphometry) methods of defining prevalence and incidence of vertebral fractures were proposed by Eastell et al. in J Bone Miner Res, 1991, 6:207-25 and Mc Closkey et al. in Osteoporos Int, 1993, 3:138-147.
The SQ method is based on evaluation of conventional radiographs by radiologists or experienced clinicians in order to identify and then classify vertebral fractures. Vertebrae T4-L4 are graded by visual inspection and without direct vertebral measurement as normal (grade 0), mild but “definite” fracture (grade 1 with approximately 20-25% reduction in anterior, middle, and/or posterior height, and 10-20% reduction in area), moderate fracture (grade 2 with approximately 25-40% reduction in any height and 20-40% reduction in area), and severe fracture (grade 3 with approximately 40% or greater reduction in any height and area). Additionally, a grade 0.5 is used to designate a borderline deformed vertebra that is not considered to be a definite fracture.
In contrast, quantitative vertebral morphometry involves making measurements of vertebral body heights on lateral radiographs. Six-point placement, the most widely used technique, utilizes manual placement of landmarks on the four corner points of each vertebral body from T4 to L4 and an additional point in the middle of the upper and lower endplates. Theses points are then used to define the anterior, middle and posterior heights of each vertebral body. The fracture grade is then derived from these three height measures or from the ratios between the heights, possibly in comparison with population-based measurements and/or normalized for inter-patient variability by comparison with measurements taken from a neighboring or reference vertebra. In clinical trials, the US Food & Drug Administration (FDA) defines a fracture as a reduction in height of 20% and more than 3 mm.
Studies have shown that a large number of fractures go undiagnosed with the current SQ and quantitative methods. More precise, objective and rapid measures of vertebral deformity are therefore needed using the automated approach of computerized analysis of digital x-ray images. Furthermore, accurate computerized vertebral fracture detection and classification may benefit from capture of vertebral shape information beyond standard 6-point morphometry, similar to the visual cues that characterize semi-quantitative vertebral assessment.
The development of a computer-assisted system for placement of the measuring points the six-point placement technique mentioned above is discussed by Kalidis L et al. in Ring EFJ (ed) Current research in osteoporosis and bone mineral measurement II, British Institute of Radiology, 1992, London, pp 14-16. The procedure is based on an algorithm that automatically locates the vertebral body contour in the digitized X-ray image and then is checked by the operator for accuracy. Correction is possible through operator intervention at any time. The system also performs additional geometric calculations, enhancing the diagnostic capability of quantitative vertebral morphometry.
More recently, Kasai et al. reported another approach based on edge detection in Proc. SPIE 6144. Their computerized scheme is based on the detection of upper and lower edges of vertebrae on lateral chest images. A curved rectangular area which included a number of visible vertebrae was identified. This area was then straightened such that the upper and lower edges of the vertebrae were oriented horizontally. For detection of vertebral edges, line components were enhanced, and a multiple thresholding technique followed by image feature analysis was applied to the line enhanced image. Finally, vertebral heights determined from the detected vertebral edges were used for characterizing the shape of the vertebrae and for distinguishing fractured from normal vertebrae.
In Radiology, 1999, 211:571-578, Smyth et al. describe the development of a technique based on use of an active shape model (ASM). An ASM is a statistical model that describes “what an object looks like” in terms of its shape and its imaging appearance. The ASM was applied to the measurement of vertebral shape on lateral Dual Energy X-Ray Absorptiometry (DXA) scans of the spine and contained 73 landmark points. The full vertebral shape description was found to be marginally more effective than was the vertebral height description for distinguishing fractured form normal vertebrae. In Investigative Radiology, 2006, 41 (12), Roberts et al. statistically modeled the shape and appearance of vertebrae on 250 lateral DXA scans using a sequence of active appearance models (AAMs) of vertebral triplets. The models were matched to unseen scans given an approximate initial location of the centre of each vertebra. Each vertebral contour used 40 points around the vertebral body with 8 further points around the pedicles for L4-T10, and 32 points per vertebra for T9-T7. The authors do not address endplate shape, but rather, focus on the image texture.
DXA imaging has some advantages over conventional radiography, namely a lower radiation dose and less distortion of the vertebral body images by projection artifact. However, radiography is still preferred in the diagnosis of vertebral fracture due to its higher imaging resolution and lower noise in image formation which means that, in contrast to DXA images, a radiographic image can depict the collapse of the cortical endplate.
There have been a number of attempts to detect and measure vertebrae in radiographs using statistical models of shape and appearance. Long et al. in Proc. SPIE 3979:169-179 describe constructing an ASM in the researching of algorithms to segment anatomy in radiographs of cervical vertebrae to derive from the segmented data measurements useful for indexing this image set for characteristics important to researchers in rheumatology, bone morphometry, and related areas. To construct the ASM, for each vertebra, 24 points were collected; six of these correspond to the standard 6-point morphometry set (corners and superior/inferior midpoints on each vertebra). The other points consist of anterior and posterior midpoints and, around each of the corners, four additional points were collected, two on each side of a corner point. In related work, the use of AAMs is described by Zamora et al. in: Proc SPIE 5032:631-642 and by Howe et al. in Proc IEEE 6th SSIAI. 2004:182-186.
In Med Image Analysis, 2007, 11:503-512, de Bruijne et al. describe the use of pairwise conditional shape models trained on a set of healthy spines, the most likely normal vertebra shapes are estimated conditional on the shapes of all other vertebrae in the image. The difference between the true shape and the reconstructed normal shape is subsequently used as a measure of abnormality. A total of 52 landmarks was placed along the upper, anterior, and lower boundary of each vertebra, interpolated equidistantly between the four vertebral corners.
Roberts describes an extension to his work to include radiographs in Proc. Medical Image Understanding and Analysis, 2006, I:120-124. To build an AAM, each vertebral contour uses 60 points around the vertebral body with 8 further points around the pedicles. The endplate rims were modeled using a quasi-elliptical shape, rather than the single edge previously used for DXA images. The accuracy of the search was characterized by calculating the absolute point-to-line distance error for each point on the vertebral body.